How can you secure a data warehouse as an administrator?

Start with Identifying your sources and Classifying your Data. then use System Control, Data Access control, Encryption and Masking techniques to secure the DW system and its data.

First, it is essential to comprehend basic data residency requirements, as each country or region may have distinct requirements. During the modeling phase, data classification (e.g., PHI, PI, financial, proprietary, confidential, restricted, internal, contextual, and public, etc.) should be applied to each attribute. Defining a data steward for each piece of data is crucial to oversee processes such as access control, pseudonymization, transit, quality, and governance, ensuring compliance with regulatory requirements.

You cannot protect your data if you don’t know what data you have, classification of your data is the first step to securing it. Using tools available to your database allows you to identify personally identifiable or sensitive data, choose a tool that helps you to identify and classify your data

A DWH admin can follow some standard strategies such as - Data governance, Handling sensitive data, DB Level Security, Perimeter Security etc. This can vary org to org. It’s also crucial to follow rules set by industry standards like GDPR, SOC etc. to ensure data safety.

Role based access, assign specific roles and permissions to users based on their responsibilities could also be done at schema level

It’s crucial to implement robust access controls and encryption methods for data security. Ensure that sensitive data is encrypted both at rest and in transit. Employ data masking techniques, especially in development and testing environments, to protect sensitive information. Additionally, monitor access logs and set up alerts for unusual activities to preempt potential breaches.

When possible, it's always best to use highest security versions of hashing. For instance, if your architecture supports SHA-2. That would be a great way to store data and transfer data where possible to avoid de-identification of data.

Data masking helps to protect the personal identifiable information or PII from any unauthorised access. Data encryption enhances the security of data by providing an additional layer of security to data in transit. Hence an authorised person with a proper encryption key can decrypt the data at their end.

Most datawarehouses support out of the box transparent data encryption, and data masking, either at column level, row level or both. Leverage these. If extra security is required you can always rely on views, and remove user access to the final tables. Then in the view you can expose only what you want. Several cloud DWH also support the concept of Secure Views, where users have rights to see data returned by the view but not the view code itself.

Securing against accidental loss, deliberate misuse / destruction, accidental misuse / destruction, hackers / malevolent actors, etc. requires several precautions in addition to the requirements detailed on the left (backups, maksing, encryption, audits, training, etc). Categorization / classification would help in setting up Role Based Access Controls through ACLs or other features available through the database technology used for the DW. Scanning for any vulnerabilities and remediating them according to priority would help to secure systematically. All stages of SDLC need to address security towards a fully secure Data Warehouse. There are instances where the DW got messed up because of surrogate key issues.

The are multiple tools and techniques to address backup and recovery; such as BCP/DR plans, patch upgrades to the recoveries, third-party products.

It used to be the norm to not backup your data warehouse as you could simply rebuild it from source, but those days are long gone as well as some legal requirements to be able to see how a warehouse looked on a specific day, especially in financial organizations. Make sure that your backup solution addresses the requirements from a business point of view, there is no need to have hourly backups if your business doesn’t have this requirement, don’t over- or under architect it. Sometimes the out of the box solution is good enough

Tier your data. Backup dynamic data, most DWs are made of immutable data beyond a certain time. Don’t waste time/resources on the immutable tier. Only one (or two copies) in another storage tech or platform is enough.

- Have a DR plan in place and test it OFTEN - Backup data to protect against user error and accidental loss. - Define clear RPO/RTO and communicate to the bussines. Sign acceptance contracts. - Do regular tests to make sure compliance is still up to date.

This is in most cases the most overlooked step. You cannot argue with a business user when they say that the data warehouse is under performing if you aren’t auditing and monitoring your data warehouse. It is important, as with any other database to analyze your query performance and benchmark performance. Performance cost is paid somewhere, in cloud data warehouses they can scale to give you better performance, here the cost is money, optimizing your performance comes at the cost of your time, investigate and spend your costs wisely.

One good audit policy that also cuts down on warehouse storage is retention policies. For instance, a retail client may only want to keep 18 months of transactional data in their warehouse for marketing purpose. Keeping the minimal amount of required data cuts infrastructure cost as well as mitigates risk if the data was accessed by an unauthorized party.

As an admin, they don’t have much control on what data is being stored as that is other team’s job. So securing the data comes with network (Implement firewalls and network security measures to control access to the data warehouse. Restrict connections to trusted IP addresses.), access controls (mplement strict user access controls. Grant permissions based on roles, ensuring users have the minimum necessary privileges.) and encryption of data in motion. Monitoring, regular audit and backup and recovery are other major points to consider.

Protecting sensitive data require also strong authentication measures. Single sign-on (SSO) simplifies authentication, but without robust security, risks arise. Implement SSO with strong encryption for authentication tokens. Employ multi-factor authentication (MFA) for added protection. Regularly audit SSO configurations to address weaknesses. Limit the use of generic user accounts and strictly control production user credentials. Update also generic user's passwords. Extend security beyond user access to data exports. Carefully control data export permissions, enabling data masking for sensitive fields, and implementing encryption for exported data. This layered approach safeguard sensitive information from unauthorized disclosure.

- Force rotate passwords - Use vault stores, with audit trails - Enforce MFA when possible - Limit to the minimum and track any technical users if really needed, and enforce password rotation - Use RBAC models to control permissions inside the tool

In order to secure a data warehouse awareness among the stakeholders (Employees,Customers,Third party) involved plays a crucial role. Awareness addresses: 1) Insider Threat 2) Data accuracy and correctness 3) All other principles of Data protection

Data stored in the data warehouse should have restricted role based access to secure it from unauthorised access. Also the consumers of these data, which are mostly reporting tools should ensure data is protected from their end as well by limited exposure to users based on their roles and responsibilities. Data retention policy should be defined to ensure unnecessary spaces are not consumed by growing tables.

Other point that might use full is to "compliance with regulations". Ensure compliance with relevant data protection regulations, such as GDPR, HIPAA, or industry-specific standards. Understand and implement the necessary measures to meet compliance requirements.

I feel below should be done - RBAC implementation - Row level security - Column level security - Encryption - Decryption of PII/Sensitive data - Tokenization/ Detokenization of data as required. - Password rotation on certain frequency for batch and individual users - access and logs monitoring with alerting on specific thresholds - retrieval and storage of creds in vault

Ensuring the security of a data warehouse is a continuous effort that requires consistent review and improvement. While Role-Based Access Control (RBAC) is fundamental, it's essential to incorporate additional measures such as encryption (SSL/TLS, TDE) is to ensure when data is in motion should not rest followed by firewalls, VPNs, audit logging, data masking, and redaction techniques for concealing sensitive information like Personally Identifiable Information (PII). Compliance with regulations (GDPR, HIPAA) is also vital. Regular assessments and the integration of these measures enhance data warehouse security, mitigating the risks associated with unauthorized access, data breaches, and other potential threats.

Securing data warehouse involves several critical steps: *Authentication and Access Control:* Use strong authentication like Windows auth and role-based access control (RBAC) to limit user access. *Encryption:* Employ Transparent Data Encryption (TDE) for data at rest, SSL/TLS for data in transit, and column-level encryption for sensitive data. *Updates and Monitoring:* Regularly update with patches, monitor for suspicious activities, and enable auditing to track access. *Backup and Network Security:* Maintain backups for disaster recovery, configure firewalls to restrict access, and utilize network encryption protocols. *Compliance:* Adhere to industry standards (HIPAA, GDPR) and follow security guidelines from the software provider.